You don't need to pay for courses, send your contact information to experts who only want your data, or jump from videos to posts desperately trying to understand how to optimize your content for AIs (LLMs).

Gemini itself, in the 2.5 Pro model, shows the step-by-step process of generating the information, and it already has the tips you need to look at the project you are optimizing and make the adjustments, which (SURPRISE!) is what we've been saying for a long time that we need to pay attention to:

• Understanding the Domain of Knowledge or subject matter
• Make your project relevant and generate authority and reputation.

In this article, I will show you the reasoning behind this Google model, which inspired me to write this article, and I will base my understanding of the concepts of relevance, authority, and reputation for the search engine .

Distilling Gemini's reasoning to understand how to "Optimize for AI"

The process below was extracted from a voice search in the Gemini app for Android, using the experimental 2.5 pro model. The process I describe below shows the steps the model generated and the key points for finding sources to generate the answer I need:

Now let's see how the steps used by the Gemini 2.5 Pro can help us, as it (and perhaps all existing LLM models) do, to organize information and answer our questions.

Identify the main need.

The system seeks to understand the user's needs, in this case the person searching, just as we do when creating content and information for our projects. And it already understands, from the question's formatting, that the user needs reliable and potentially diverse information, specifically mentioning the need for reliable information sources.

It is necessary to recall the information search process triggered by the Information Gap, described below.

We are in a situation of lack, where we lack something to move from our current state to a new one, where the acquired information allows us to fulfill the initial need. There are many examples, but imagine you get a promotion at work and need to monitor employees who are now reporting to you.

This new development creates a need for information on how to monitor this progress. And it triggers the search process.

Therefore, it was to be expected that Gemini would seek to identify the user's main need.

Acknowledge the subject

After identifying the user's main need, the system seeks to recognize the subject the user is searching for, and here's a valuable tip on a topic I always talk about: define the knowledge domain to which your project belongs, identify related subjects, and remain true to that choice.

This is important because we're dealing with algorithms, LLMs, automated systems, all of which struggle to understand human language and its nuances. Here on the blog you'll find several articles where I discuss ambiguity and semantics , which will help you understand the need to define the topics covered and not broaden the scope of your content too much.

Formulate search queries

This is an internal thought process that translates into the actual use of the search tool, in this case, Google Search . In the screenshot, you can see that Gemini searches for relevant websites and performs a search for related terms, generating a list of these terms.

Since he previously acknowledged the topic, it leads me to believe that Google's knowledge graph may be being used here to construct the search strategy for terms related to the main topic and related topics.

This strategy is very similar to that used by a librarian when helping someone seeking assistance in the library find a book that can answer their question.

Perform the search (using the tool)

Then it generates the command:

 tool_code block with the selected queries.

And he does a Google search, just like the rest of us. So when we talk about the new versions of Gemini and all the other LLMs that access the web to improve information generation, we can say with a high degree of certainty that the strategies that contribute to a better understanding of content with the goal of indexing and retrieving it continue to be valid from this stage of LLM reasoning.

Analyze the research results.

Here, the system simulates, based on expected results, the research findings that will likely point to various types of resources that can be organized to generate the answer to my question.

We know that the system simulates the search result, suggesting that the Google search is a simulation (based on LLM database embeds?) that generates a synthetic search result, with a strong probabilistic basis ( a search that will likely point ) to resources, which can be websites, books, videos, academic articles, and other types.

The next item concludes the discussion regarding the assumption of the search simulation and the synthetic result assembled through this simulation.

Synthesize the findings and structure the response.

Here we have the largest volume of tips on the strategies that Gemini uses.

• Prioritize Authority: Start with the most official sources and with scientific guidance.
  • Depending on the type of search, the system is directed to select some very specific categories of sources.
• Presenting Information Portals: Mention reliable websites. ​
  • Due to the nature of my question (involving health-related topics), the system takes precautions regarding the reliability of the accessed portals, indicating that some portals are selected and marked as trustworthy.
• Consider Associations of {subject}
  • Again, a semantic clustering strategy, creating associations with the main subject, perhaps to expand the search or even test new approaches.
• Customize (subtly): Where Gemini uses my profile to personalize responses for me.
  • Here's where the functionality in paid versions of LLMs differs from the free versions. I can create a profile of myself and save it in the tool, which will always personalize the search according to the information I've given it about myself.
• Offer additional assistance: Ask if the user has specific questions about {topic}..
  • Another extreme difference between information retrieval and information generation: the system's ability to have memory and prompt a conversation that expands on the subject or addresses new aspects.
• Refine the language: Ensure the language is clear, direct (according to the user's preference), and uses appropriate Portuguese terminology. Use Markdown for structure (bulletins).
  • Another aspect of personalization is adapting the language to my profile as described on the platform, and organizing the output format for human reading, using a structure that makes it easier to scan the response.

In Google's Search Quality Evaluator Guidelines document, you can learn what relevance, authority, and reputation mean to Google.

Based on the Guidelines for Research Quality Assessors, relevance, authority, and reputation play crucial roles in how Google evaluates the quality of search results. These concepts are intrinsically linked to the goal of providing users with useful and reliable information.

search quality analyzers .

What does relevance mean to Google?

Relevance is fundamental to determining whether a search result meets the user's needs.3 The guidelines emphasize that evaluators should focus on user needs and assess how useful and satisfactory a result is for the specific query.3 A query may have multiple interpretations and user intentions, and the results should be relevant to the dominant, common, or even a reasonable minority interpretation.

The “Needs Met” rating scale ranges from “Fully Meets” to “Fails to Meet.” A result that is completely off-topic for the query or addresses an unlikely interpretation of the query is classified as “Fails to Meet.” Conversely, a result that is very useful for any dominant, common, or less reasonable interpretation of the query/user intent is classified as “Highly Meets.”

Relevance is not limited to keyword matching alone. Evaluators are instructed to use common sense and web research to understand the query and user intent. They should consider whether people in different locations or with different contexts might be searching for something different with the same query.4 Furthermore, relevance can change over time as the meaning of queries can evolve.

In short, relevance ensures that the results presented by Google are useful and match what the user is looking for, whether it's specific information, a website, a product, or a physical location.

Screenshots from Google's official documentation that guides search quality analyzers.

What does Authority mean to Google?

Authority is an essential component of the E- EAT ( Experience , Expertise, Authority, and Trustworthiness), which is one of the pillars of evaluating page quality. Authority refers to the degree to which the content creator or website is recognized as a reliable source on a given topic.

The guidelines explain that while most topics don't have a single "official" and authoritative website or content creator, when one exists, that source is often one of the most reliable. Examples include the official government page for passport renewals or a local business's social media profile as an authoritative source for information on current promotions.

For YMYL (“Your Money or Your Life”) topics, a website's authority should be judged by what experts in the field have to say. Recommendations from specialized sources, such as professional societies, are strong evidence of a positive reputation and, by extension, authority.

When assessing authority, evaluators should consider whether the content creator possesses the necessary knowledge or skills for the topic. Different topics require different levels and types of expertise to be considered trustworthy. Authority contributes significantly to the overall trustworthiness of a page. An authoritative website or content creator is seen as a "go-to" source for information on that topic, which increases the likelihood that the content is accurate and useful.

Screenshots from Google's official documentation that guides search quality analyzers.

What does Authority mean to Google?

The reputation of the website and its content creators is another crucial factor in evaluating page quality. The guidelines instruct evaluators to research reputation using external and independent sources. This includes looking for news articles, references, expert recommendations, and other credible information written by people about the website or the content creator.

Reputation research should be conducted according to the page's topic. A website may be a reliable source for one type of content (e.g., funny videos) but not reliable for another (e.g., financial information).

Customer reviews are also important for assessing the reputation of stores, companies, or any website that offers products or services. A large number of positive and detailed user reviews can be considered evidence of a positive reputation. On the other hand, credible reports of fraud or financial misconduct indicate an extremely negative reputation.

Reputation is especially important for detecting unreliable websites and content creators. Even if the content appears good superficially, reputation research can expose scams, fraud, or other signs of harm. An extremely negative reputation for a website or content creator can lead to the page being ranked as "Lowest," as many users would consider the site or page untrustworthy.

What does this mean for the projects I optimize?

In short, reputation helps Google understand how others perceive a specific website or piece of content, contributing to the overall assessment of the reliability and quality of the information.

Together, relevance, authority, and reputation are fundamental elements for search quality evaluators, who use these criteria to help Google refine its algorithms. By identifying relevant content from authoritative and reputable sources, Google seeks to offer users the best search experience, according to them.

But one thing is certain: offering answers with accurate, reliable information that meets their needs is the way to have users satisfied with the tool.

But what I want to tell you is that it's not necessary to change the way you refer to SEO , nor to dive headfirst into every new thing some guru or company tries to sell us. It's necessary to understand how the tools work, change the concept of outdated practices, and do a lot of testing. Maybe when search is 100% generated by LLM (Learning Management Language), some things will change completely; maybe when all that's left are agents that talk to other agents and we just send a problem and receive the solution, we'll need to completely reinvent ourselves. But for now, I'll stay calm, study, and test a lot.